Gut microbiome signature of metabolically healthy obese individuals according to anthropometric, metabolic and inflammatory parameters

In this study, we investigated the characteristics of gut microbiome in the metabolically healthy obese (MHO) patients, and how they correlate with metabolic and inflammatory profiles. A total of 120 obese people without metabolic comorbidities were recruited, and their clinical phenotypes, metabolic and inflammatory parameters were analysed. The faecal microbial markers originating from bacterial cell and extracellular vesicle (EV) were profiled using 16S rDNA sequencing. The total study population could be classified into two distinct enterotypes (enterotype I: Prevotellaceae-predominant, enterotype II: Akkermansia/Bacteroides-predominant), based on their stool EV-derived microbiome profile. When comparing the metabolic and inflammatory profiles, subjects in enterotype I had higher levels of serum IL-1β [false discovery rate (FDR) q = 0.050] and had a lower level of microbial diversity than enterotype II (Wilcoxon rank-sum test p < 0.01). Subjects in enterotype I had relatively higher abundance of Bacteroidetes, Prevotellaceae and Prevotella-derived EVs, and lower abundance of Actinobacteria, Firmicutes, Proteobacteria, Akkermansia and Bacteroides-derived EVs (FDR q < 0.05). In conclusion, HMO patients can be categorised into two distinct enterotypes by the faecal EV-derived microbiome profile. The enterotyping may be associated with different metabolic and inflammatory profiles. Further studies are warranted to elucidate the long-term prognostic impact of EV-derived microbiome in the obese population.


Gut microbiome signature of metabolically healthy obese individuals according to anthropometric, metabolic and inflammatory parameters
In this study, we investigated the characteristics of gut microbiome in the metabolically healthy obese (MHO) patients, and how they correlate with metabolic and inflammatory profiles.A total of 120 obese people without metabolic comorbidities were recruited, and their clinical phenotypes, metabolic and inflammatory parameters were analysed.The faecal microbial markers originating from bacterial cell and extracellular vesicle (EV) were profiled using 16S rDNA sequencing.The total study population could be classified into two distinct enterotypes (enterotype I: Prevotellaceaepredominant, enterotype II: Akkermansia/Bacteroides-predominant), based on their stool EV-derived microbiome profile.When comparing the metabolic and inflammatory profiles, subjects in enterotype I had higher levels of serum IL-1β [false discovery rate (FDR) q = 0.050] and had a lower level of microbial diversity than enterotype II (Wilcoxon rank-sum test p < 0.01).Subjects in enterotype I had relatively higher abundance of Bacteroidetes, Prevotellaceae and Prevotella-derived EVs, and lower abundance of Actinobacteria, Firmicutes, Proteobacteria, Akkermansia and Bacteroides-derived EVs (FDR q < 0.05).In conclusion, HMO patients can be categorised into two distinct enterotypes by the faecal EV-derived microbiome profile.The enterotyping may be associated with different metabolic and inflammatory profiles.Further studies are warranted to elucidate the long-term prognostic impact of EV-derived microbiome in the obese population.

ApoA1
Apolipoprotein The mean serum triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) levels were 123.7 mg/dl, 118.5 mg/dl, and 55.7 mg/dl, respectively.The median apolipoprotein-A1, and apolipoprotein-B levels were measured to be 142.5 mg/dl and 99 mg/dl, respectively.Body fat composition was measured and calculated using CT and dual energy X-ray absorptiometry (DEXA).The visceral fat area, subcutaneous fat area, and the visceral to total body fat percentage were calculated to be 117.0cm 2 , 275.9 cm 2 , and 30.15% on the median, respectively.Mean body fat mass measured by DEXA was 28.7 kg and mean body fat percentage was 40.6%.
We analysed the correlation between different phenotypic, inflammatory parameters (Supp.Figure 1).The BMI level shows a positive correlation with visceral fat area, subcutaneous fat area, waist circumference, and waist-to-hip ratio.The BMI also positively correlates with levels of IL-1β, IL-6, TG, and TG/HDL-C ratio.In contrast, HDL-C, and Apoprotein-A1 levels inversely correlates with apolipoprotein-B/apolipoprotein-A1 ratio, TG/HDL-C ratio, and BMI level (all p < 0.05 by Spearman's rank correlation test).

EV-derived microbiota composition
The faecal microbiome originating from bacterial cells and extracellular vesicles (EV) was profiled using 16S rDNA sequencing.There were no significant findings when analysing the microbiome originating from bacterial cells (data not shown).In contrast, when the correlation between faecal bacterial EV-derived microbiome composition and host phenotype was analysed, there were some significant correlations as the following (Fig. 1A-C).Clinical phenotypes, including serum level of IL-1β and resistin showed, significant correlation with the abundance of different faecal EV-derived microbiota of different species.A list of significant correlations between taxa and clinical variables with p < 0.05 is provided in Supplementary Table 1.Some of them are as follows: on the phylum level, the abundance of Firmicutes-derived EVs showed positive correlations with visceral fat area, serum apolipoprotein-B/apolipoprotein-A1 ratio, apolipoprotein-B, serum LDL-C level and serum IL-1β level (Spearman's rank correlation coefficient ρ = 0.18, 0.19, 0.22, 0.24 and 0.24 respectively, all p < 0.05).On the genus level, the abundance of Akkermansia-derived EVs showed negative correlations with BMI and subcutaneous fat area (ρ = − 0.19, − 0.23 respectively, all p < 0.05).The abundance of Akkermansia-derived EVs also showed a negative correlation with serum IL-1β, leptin, fasting insulin, HOMA-IR, and resistin level (ρ = − 0.44, − 0.21, − 0.23, − 0.24 and − 0.32 respectively, all p < 0.05).The abundance of Bacteroides-derived EVs also showed weak positive correlations with serum leptin level (ρ = − 0.23, p = 0.01).The abundance of Prevotella-derived EVs also showed weak positive correlations with serum IL-1β level (ρ = 0.19, p = 0.04).Among them, however, only negative correlations between the abundance of EV-derived Akkermansia, and serum resistin and IL-1β levels remained significant after FDR adjustment (FDR q < 0.01).

Gut microbe-derived extracellular vesicles in different enterotypes
The overall study population could be classified into two distinct enterotypes based on their stool EV-derived microbiome profile (enterotype I: Prevotellaceae-predominant, enterotype II: Akkermansia/Bacteroides-predominant, Fig. 2).The Calinski-Harabasz (CH) index was used to calculate the optimal number of clusters (Fig. 2A).Out of a total of 120 subjects, 34 were classified as enterotype I, and 86 were classified as enterotype II.In contrast, the bacterial cell-derived microbial compositions failed to separate the study population into distinct subgroups of patients (Supp.Figure 2A-C).
We compared the species richness and evenness of the bacterial EV-derived microbiome between the two enterotype groups (Fig. 3A).The microbial diversity calculated by the Shannon index and Faith's phylogenetic diversity were both significantly lower in enterotype I than in enterotype II (p < 0.001 and p = 0.003, respectively).The microbial composition, analysed by unweighted and weighted UniFrac distance, is depicted in Fig. 3B,C.Statistical analysis revealed a distinct distribution between the two enterotypes (PERMANOVA, all p = 0.001).
We analysed the relative abundance of gut microbe-derived EVs at the phylum, family, and genus levels (Fig. 3D, Supp.Table 2).Enterotype I subject showed significant enrichment of Bacteroidetes-derived EVs, and depletion of Actinobacteria, Firmicutes and Proteobacteria-derived EVs in phylum level (all FDR q < 0.05).Among the phylum Bacteroidetes, subjects in enterotype I showed a higher abundance of Prevotellaceae-derived EVs at the family level and Prevotella-derived EVs at the genus level (all FDR q < 0.05).At the family level, subject in enterotype II had a higher abundance of Lachnospiraceae and Ruminococcaceae-derived EVs (all FDR q < 0.05).At the genus level, subjects in enterotype II a had significantly higher abundance of Akkermansiaderived EVs (FDR q < 0.05).

Enterotype and host metabolic and inflammatory markers
The metabolic and inflammatory markers as well as the body fat compositions according to the enterotypes are summarised in Table 2.The enterotypes were independent of patient age and sex.Subjects in enterotype I tended to have higher levels of BMI, which did not reach statistical significance (p = 0.060).
Although no significant difference was seen, serum resistin tended to be higher in subjects in enterotype I compared to subjects in enterotype II (nominal p = 0.096).Serum IL-1β levels were higher in subjects in enterotype I than in those in enterotype II (nominal p = 0.025 and FDR q = 0.050).In contrast, there was no significant difference in serum IL-6 levels between the two enterotype groups (p = 0.622).
The total body fat mass also tended to be higher in the enterotype I group than in enterotype II group (nominal p = 0.068).Both visceral fat area and subcutaneous fat area did not differ between the two enterotypes.There was no difference in the dietary intake of total calories, carbohydrates, lipids, proteins, fibers, or total cholesterols.Further analysis on the clinical variables were performed according to sex (Supp.Table 3).The distribution of overall anthropometric measurements, metabolic parameters, and inflammatory parameters by enterotype showed generally similar trends in both men and women.In men, however, BMI and waist circumference were significantly lower in enterotype II (nominal p = 0.002 and 0.028, respectively, Supp.Table 3), and HDL-C level were significantly higher in enterotype II (nominal p = 0.024, Supp.Table 3).In women, BMI and waist circumference failed to show statistically significant difference (p > 0.05, Supp.Table 3).The distribution of some phenotypic and inflammatory markers in faecal EV-derived microbiome is visually depicted in Fig. 4. The microbiome profile of the study participants appears to be largely divided into two clusters.Interestingly, the distribution of faecal EV-derived microbiome profiles according to serum IL-1β levels   www.nature.com/scientificreports/appeared to be markedly different in the two clusters (Fig. 4D).The distribution of faecal EV-derived microbiome profiles did not differ according to the BMI (Fig. 4A), waist circumference (Fig. 4B) or serum IL-6 levels in the two enterotypes (Fig. 4C).
In the case of the bacterial cell-derived microbiome, there were no significant findings based on anthropometric and inflammatory biomarkers of obesity (Supp.Figure 3A-D).

Discussion
In this cross-sectional study, we have shown the characteristics of faecal EV-derived microbial composition in metabolically healthy obese individuals.For example, the abundance of Akkermansia-derived EVs negatively correlated with subcutaneous fat area as well as total BMI; they also negatively correlated with serum IL-1β, leptin, fasting insulin, HOMA-IR and resistin levels (Fig. 1, Supplementary Table 1).On the other hand, the abundance of Prevotella-derived EVs positively correlated with serum IL-1β levels.
The gut Akkermansia is reported to be associated with a healthier clinical profile, and its abundance is decreased in obese patients [27][28][29] .The anti-inflammatory effects of Akkermansia-derived EVs have been previously reported in various studies [22][23][24] .A. muciniphila is known to enhance gut epithelial barrier function and shows anti-inflammatory effects [30][31][32] .Our findings are consistent with these previous studies in that the abundance of Akkermansia-derived EVs negatively correlates with BMI and serum levels of inflammatory cytokines.
On the contrary, the relative abundance of Prevotella has been reported to be associated with increasing BMI in obese patients 16,28,33 .Conying Chen et al. have reported an increased relative abundance of P. copri in obese pig models, compared to that of non-obese pigs, and the abundance of P. copri was associated with the serum metabolite levels associated with obesity 34 .Similary, De Vadder et al. have used germ-free mice and reported Table 2. Characteristics of the subjects according to enterotypes.Categorical variables were expressed as number of subjects (n), (%), and compared using χ 2 -test.Continuous variables are expressed as mean ± standard deviation, and compared using Student's t-test, unless otherwise noted.The FDR is calculated by adjusting raw p values with Benjamini-Hochberg method.FDR false discovery rate, BMI body mass index, sBP systolic blood pressure, dBP diastolic blood pressure, LDL-C low-density lipoprotein cholesterol, HDL-C high-density lipoprotein cholesterol, TG triglyceride, AST aspartate aminotransferase, ALT alanine aminotransferase, ApoA1 apolipoprotein A1, ApoB apolipoprotein B, HOMA-IR Homeostatic Model Assessment for Insulin Resistance, HOMA-β Homeostasis model assessment of β-cell function, IL-6 interleukin-6, IL-1β interleukin-Iβ, IQR interquartile range.Bold style indicates statistical significance.† Phenotypes not following normal distribution were compared using non-parametric analysis: Wilcoxon ranksum test.These variables are expressed as median (IQR).† † Alcohol consumption includes both current and past alcohol consumption.that the host chronic inflammatory response deteriorated in P. copri-gavaged mice.The study also revealed that succinate produced by P. copri improves glucose metabolism and insulin sensitivity in obese mice 35 .
In this study, the study participants were a relatively homogenous group of obese individuals with no metabolic or cardiovascular comorbidities.The faecal bacterial cell-derived microbial composition did not show any distinction in the overall study population.However, the study subjects could be grouped into two distinct groups, called enterotypes, based on their faecal bacterial EV-derived microbiome composition.The faecal EVderived microbial composition of the two enterotypes showed significant differences in the relative abundance of Prevotella and Akkermansia-derived EVs.
This result is consistent with previous studies on the gut microbiome of obese patients.Lars Christensen et al. reported that obese people can be categorised into two groups by the gut microbiome 36 .Manimozhiyan Arumugam et al. also reported distinct enterotype groups in obese subjects, and Bacteroides, Prevotella, and Ruminococcus were the main contributors to the differentiation between the two enterotypes 37 .Lars Christensen et al. has reported that Prevotella-rich group is associated with high dietary carbohydrate, resistant starch, and fiber.Bacteroides-rich enterotype was associated with high dietary fat and low dietary fiber.In this study, patient reported dietary intake of carbohydrate, fiber and fat content failed to show significant difference between the two enterotypes.The negative results of our study might be attributed to the relatively small sample size of the study.Another possible reason could be a recall bias as we investigated dietary intake using self-reported survey results.
Previous studies have demonstrated that the gut microbiome may influence the metabolic health of the host through various interactions.Bacterial cell-derived microbiome includes DNAs from dead or inactive bacteria as well as living bacteria.In contrast, analysis of bacterial DNA in the EVs are believed to reflect the actual activity of bacteria in the faecal material 38,39 .Recent studies have provided evidence that the EVs from the gut microbiome play an important role in the interaction between the gut microbiome and host metabolism 20,22 .It has been reported that EV-derived microbial composition, rather than faecal bacterial DNA, is more representative of actual bacterial activity and its impact on host health conditions [40][41][42] .Nevertheless, there are not enough studies on how EV-derived 16S ribosomal DNA profiles are associated with obesity or related metabolic complications.In this study, the microbial composition analysis of the bacterial cell-derived microbiome did not show definite clustering (Supp.Fig S2 ), whereas EV-derived microbial composition can be used to discriminate the obese population into two groups.
The differences in clinical parameters, including inflammatory cytokines and body fat composition, between these two enterotypes were also analysed in this study.Interestingly, the two enterotypes showed significant differences in serum IL-Iβ levels.IL-Iβ is an inflammatory cytokine that is activated by inflammasomes.The activation of IL-Iβ is known to be a key process that contribute to the initiation of insulin resistance and type 2 diabetes 43 .Moreover, evidence suggests that the inflammasome and IL-Iβ are linked to obesity-related diseases 44,45 .While no other clinical parameters showed significant difference between the two enterotypes, this preliminary results, difference in IL-1β might imply possibility that the two enterotypes might have different systemic inflammatory, metabolic state, and thereby resulting in difference in long-term clinical outcomes.
Interestingly, when analysing metabolic and inflammatory parameters according to the enterotype, some differences were observed by sex.Although trends in metabolic and inflammatory parameters were similar in both sex groups, BMI, waist circumference, and HDL-C levels showed statistical differences only in men.Additionally, a few clinical parameters, apolipoprotein-B, HOMA-IR, and visceral/total body fat ratio might have different distribution by sex, but all of them failed to show statistical significance to draw any conclusion.The physiologic difference in the sex hormone levels and body fat composition between men and women may contribute to the differences in clinical parameters and EV-derived microbiome by sex.However, our study lacked in the number of subjects to perform any further subgroup analyses, thus further studies with larger number of subjects may be able to elucidate the difference in men and pre/post-menopausal women.
The limitations of this study must be acknowledged.First, multiple testing on serum IL-1β levels between the two enterotypes revealed an FDR q-value of 0.050.This lack of statistically significant results might be attributed to the limited number of study subjects enrolled in the study, which limits generalisation of the result or further subgroup analysis.Second, our findings suggest that EV-derived microbial composition and metabolic parameters might have different clinical implications in male and female groups.However, our study population had sex discrepancies and there were not enough male subjects to perform additional subgroup analysis by sex.Furthermore, the cross-sectional study design could not show the impact of different EV-derived microbial compositions on short-term, or long-term prognosis.Further studies with larger number of obese patients and long-term follow-up period would further clarify the correlation between different metabolic conditions and EV-derived microbial composition.
In conclusion, the EV-derived microbial abundance shows association with different anthropometric, metabolic, and inflammatory parameters in metabolically healthy obese subjects.Our findings show that MHO individuals can be categorised into two discrete groups based on their faecal bacterial EV-derived microbial composition.The two enterotypes may have difference in their IL-1β levels, but did not show statistical differences in other anthropometric, metabolic, or inflammatory parameters.Although this study results remain inconclusive, it suggests the possibility to uncover relationships between EV-derived microbiomes and metabolic parameters or long-term outcomes in MHO subjects.Further studies with larger number of subjects and analysis might elucidate the impact of EV-derived microbial composition on metabolic parameters or long-term prognosis in MHO subjects.

Figure 1 .
Figure 1.Correlation between clinical/laboratory parameters and microbial abundances.Correlation between different clinical parameters were analyzed by Spearman's rank correlation analysis.Statistically significant correlation with FDR q value < 0.05 are indicated by asterisks.(A) Correlation in phylum level, (B) Correlation in family level.(C) Correlation in genus level.Dendrograms on X, Y axis were generated using complete-linkage hierarchical clustering.WC waist circumference, BMI body mass index, IL-1B interleukin-1β, WHR waist-tohip ratio, dBP diastolic blood pressure, sBP systolic blood pressure, ALT alanine aminotransferase, AST aspartate aminotransferase, TG triglyceride, HDL-C high-density lipoprotein, LDL-C low-density lipoprotein, IL-6 interleukin-6.

Figure 2 .
Figure 2. Enterotyping of the study subjects by extracellular vesicle-derived microbial compositions.(A) Calinski-Harabasz (CH) index.(B) Principal coordination analysis (PCoA) plot showing enterotype distribution of the obese population by Jensen-Shannon Divergence distance.The distance of one grid corresponds to 0.2 in Jensen-Shannon Divergence distance (d = 0.2).

Figure 4 .
Figure 4. EV-derived microbiome profile and distribution of clinical parameters and inflammatory markers in MHO subjects.(A) Weighted UniFrac distance matrix showing distribution of body mass index.(B) Weighted UniFrac distance matrix showing distribution of waist circumference.(C) Weighted UniFrac distance matrix showing distribution of interleukin-6.(D) Weighted UniFrac distance matrix showing distribution of interleukin-1β.